Study on Extraction of Urban Vegetation Based on Random Forest Combining Multiple Features
There are few applications of GF6-WFV images produced in China in urban vegetation extraction research,and a single fea-ture containing information cannot better obtain distribution information of vegetation. Based on GF6-WFV images,this article extracts spectrum,commonly used vegetation indices,and red edge vegetation indices,constructs a random forest model with multiple feature combinations,and conducts research on urban vegetation extraction. The results indicate that:(1) When extracting vegetation using multiple feature combinations,the spectral band combined with commonly used vegetation indices and red edge vegetation indices has the highest extraction accuracy,with an overall accuracy of 87.3％,a Kappa coefficient of 0.7386,and a vegetation extraction accu-racy of 80.57％. (2) Compared to commonly used vegetation indices,the red edge vegetation index has the highest relative accuracy in information extraction. Therefore,this study provides a method with good application value for extracting urban vegetation informa-tion.
multiple featuresrandom foresturban vegetationred edge vegetation indices
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